CN109212500B - High-precision KA-STAP (K-ary-based adaptive-noise) covariance matrix estimation method based on sparse reconstruction - Google Patents
High-precision KA-STAP (K-ary-based adaptive-noise) covariance matrix estimation method based on sparse reconstruction Download PDFInfo
- Publication number
- CN109212500B CN109212500B CN201810896127.4A CN201810896127A CN109212500B CN 109212500 B CN109212500 B CN 109212500B CN 201810896127 A CN201810896127 A CN 201810896127A CN 109212500 B CN109212500 B CN 109212500B
- Authority
- CN
- China
- Prior art keywords
- covariance matrix
- clutter
- stap
- sparse reconstruction
- space
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Active
Links
- 239000011159 matrix material Substances 0.000 title claims abstract description 56
- 238000000034 method Methods 0.000 title claims abstract description 37
- 238000001228 spectrum Methods 0.000 claims abstract description 36
- 230000009467 reduction Effects 0.000 claims abstract description 23
- 238000001514 detection method Methods 0.000 claims abstract description 15
- 238000001914 filtration Methods 0.000 claims abstract description 8
- 238000004422 calculation algorithm Methods 0.000 claims abstract description 6
- 238000012216 screening Methods 0.000 claims abstract description 4
- 239000013598 vector Substances 0.000 claims description 26
- 238000012545 processing Methods 0.000 claims description 20
- 230000001427 coherent effect Effects 0.000 claims description 5
- 238000005070 sampling Methods 0.000 claims description 4
- 238000010606 normalization Methods 0.000 claims description 3
- 238000013139 quantization Methods 0.000 claims description 3
- 230000009466 transformation Effects 0.000 claims description 3
- 230000001131 transforming effect Effects 0.000 claims description 3
- 238000005457 optimization Methods 0.000 claims description 2
- 230000001629 suppression Effects 0.000 abstract description 3
- 238000012549 training Methods 0.000 description 5
- 230000003044 adaptive effect Effects 0.000 description 4
- 238000013461 design Methods 0.000 description 2
- 230000000694 effects Effects 0.000 description 2
- 239000012535 impurity Substances 0.000 description 2
- 238000004088 simulation Methods 0.000 description 2
- 238000012935 Averaging Methods 0.000 description 1
- 238000007476 Maximum Likelihood Methods 0.000 description 1
- 238000009825 accumulation Methods 0.000 description 1
- 238000004364 calculation method Methods 0.000 description 1
- 238000006243 chemical reaction Methods 0.000 description 1
- 230000008878 coupling Effects 0.000 description 1
- 238000010168 coupling process Methods 0.000 description 1
- 238000005859 coupling reaction Methods 0.000 description 1
- 238000002592 echocardiography Methods 0.000 description 1
- 238000005516 engineering process Methods 0.000 description 1
- 238000005259 measurement Methods 0.000 description 1
- 230000008569 process Effects 0.000 description 1
- 230000005855 radiation Effects 0.000 description 1
- 238000011084 recovery Methods 0.000 description 1
- 238000011160 research Methods 0.000 description 1
- 230000001052 transient effect Effects 0.000 description 1
Images
Classifications
-
- G—PHYSICS
- G01—MEASURING; TESTING
- G01S—RADIO DIRECTION-FINDING; RADIO NAVIGATION; DETERMINING DISTANCE OR VELOCITY BY USE OF RADIO WAVES; LOCATING OR PRESENCE-DETECTING BY USE OF THE REFLECTION OR RERADIATION OF RADIO WAVES; ANALOGOUS ARRANGEMENTS USING OTHER WAVES
- G01S7/00—Details of systems according to groups G01S13/00, G01S15/00, G01S17/00
- G01S7/02—Details of systems according to groups G01S13/00, G01S15/00, G01S17/00 of systems according to group G01S13/00
- G01S7/41—Details of systems according to groups G01S13/00, G01S15/00, G01S17/00 of systems according to group G01S13/00 using analysis of echo signal for target characterisation; Target signature; Target cross-section
-
- Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
- Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
- Y02A—TECHNOLOGIES FOR ADAPTATION TO CLIMATE CHANGE
- Y02A90/00—Technologies having an indirect contribution to adaptation to climate change
- Y02A90/10—Information and communication technologies [ICT] supporting adaptation to climate change, e.g. for weather forecasting or climate simulation
Abstract
The invention discloses a high-precision estimation method of a KA-STAP (K-ary-start-adaptive noise) covariance matrix based on sparse reconstruction, which comprises the following steps: s1, analyzing echo data of an airborne radar, and acquiring a high-resolution two-dimensional space-time spectrum by utilizing sparse reconstruction; s2, screening pixel points on the two-dimensional space-time spectrum, and calculating a weighting value corresponding to the pixel points; s3, fitting the clutter track by using a weighted least square method; s4, estimating noise power according to the sparse reconstruction space-time spectrum, and constructing a priori clutter plus noise covariance matrix; s5, performing self-adaptive filtering and target detection on the detection unit by using the priori noise covariance matrix and the dimension reduction STAP. Compared with the conventional STAP algorithm, the method has small operand, can effectively improve clutter suppression and target detection performance of the STAP system in a non-stationary clutter environment, and is easy to implement engineering.
Description
Technical Field
The invention belongs to the technical field of knowledge-aided space-time adaptive processing (KA-STAP), and particularly relates to a high-precision estimation method of a KA-STAP noise covariance matrix based on sparse reconstruction.
Background
STAP (Space-Time Adaptive Processing ) is an important technical means for the current airborne radar to suppress ground clutter and realize ground moving target detection. The conventional STAP method is based on the assumption that clutter samples of adjacent distance units and clutter in a unit to be detected meet statistical stability, and utilizes maximum likelihood estimation of a clutter covariance matrix to solve the self-adaptive weight. To ensure that the output signal-to-noise ratio (SCNR) loss of the relatively optimal STAP process is limited to a range of 3dB, the training samples used to estimate the clutter covariance need to meet the condition of independent co-distribution (IID) and the number should exceed more than two times the number of adaptive processor dimensions.
However, in practical applications, the environment where the airborne radar is located is complex, the assumption of statistical stationarity cannot be satisfied, and it is difficult to obtain a sufficient number of IID samples, which degrades the performance of the conventional STAP method. Aiming at the problem, the scholars at home and abroad develop and research on KA-STAP, and the KA-STAP utilizes the prior information such as radar parameters, terrain information, digital map, surface coverage data and the like to assist the design of a filter, so that the clutter suppression performance of the STAP technology in a non-uniform clutter environment is effectively improved. For example, using prior information such as Digital Terrain Elevation Data (DTED), ground coverage/ground usage data (LCLU), etc. to select a uniform sample, further estimating an interference covariance matrix of a detection unit, fusing prior knowledge and observation data in a parameterized model to capture transient characteristics of a distance unit to be detected, constructing a prior clutter plus noise covariance matrix by means of the prior information, and using the sample estimation covariance matrix and the prior covariance matrix in a filter design by means of a linear weighting mode.
Disclosure of Invention
Aiming at the problem of STAP performance reduction caused by short-range clutter non-stationarity of an airborne non-positive side view array radar, the invention provides a KA-STAP noise covariance matrix high-precision estimation method based on sparse reconstruction.
In order to solve the technical problems, the invention adopts the following technical means:
a KA-STAP (high-precision-STAP) noise covariance matrix high-precision estimation method based on sparse reconstruction specifically comprises the following steps:
s1, analyzing echo data of an airborne radar, and acquiring a high-resolution two-dimensional space-time spectrum by utilizing sparse reconstruction;
s2, screening pixel points on the two-dimensional space-time spectrum, and calculating a weighting value corresponding to the pixel points;
s3, fitting the clutter track by using a weighted least square method;
s4, estimating noise power according to the sparse reconstruction space-time spectrum, and constructing a priori clutter plus noise covariance matrix;
s5, performing self-adaptive filtering and target detection on the detection unit by using the prior noise covariance matrix and the dimension reduction STAP.
Further, the step S1 is specifically implemented by the following steps:
s11, windowing echo signals received by the airborne radar, and receiving signals T_X after processing l The formula is satisfied:
T_X l ＝X l ·T w ＝[S Tl_1 …S Tl_i …S Tl_K ] N×K (1)
wherein ,Xl Is the received signal of the first distance unit, T w Is based on window function T f ＝[w(1),w(2),…,w(i),…,w(K)]Windowing matrix for K x K dimensions of diagonal elements, w (i) being the window function coefficient, S Tl_i The output signal is the output signal of the ith pulse of the ith distance unit after windowing, i=1, 2, …, M, i=1, 2, …, K, M is the total number of distance units, N is the number of array elements of the radar antenna, and K is the number of time domain pulses in one coherent processing interval.
S12, transforming matrix F by FFT domain D Obtaining an array element-Doppler domain output signal:
D_X l ＝T_X l ·F D ＝[S Dl_1 …S Dl_i …S Dl_K ] N×K (2)
wherein ,SDl_i Indicating the output signal of each array element of the ith Doppler unit of the ith distance unit.
S13, utilizing a convex optimization algorithm to perform S Dl_i And performing sparse reconstruction, wherein a constraint equation of the sparse reconstruction is as follows:
wherein ,σi Is the spatial amplitude distribution of the signal received by the ith Doppler unit of the ith range unit,
is the value of the variable x corresponding to the function g (x) taking the minimum value, and observes the matrix ψ i Is an overcomplete base composed of airspace guide vectors, and the size is N multiplied by N s ，N s Is the number of units of space domain quantization, I.I 1 Represents L 1 Norm operation, epsilon i Is an allowable error.
S14, performing space domain sparse reconstruction on the array element-Doppler domain data unit by unit Doppler to obtain a high-resolution two-dimensional space-time spectrum.
Further, the step S2 is specifically implemented by the following steps:
s21, selecting the pixel point with the largest amplitude on each normalized space frequency on the two-dimensional space-time spectrum and recording the coordinates of the pixel point.
S22, removing noise pixel points deviating from clutter tracks in the pixel points selected in the S21 to obtain a filtered point set { (x) j ,y j )}，x j Is the abscissa of the jth pixel point, y j Is the ordinate of the j-th pixel, j=1, 2, 3..n I ，N I The number of the pixel points meeting the requirements is selected.
S23, carrying out normalization processing on the space-time spectrum amplitude value vector A to obtain a normalized space-time spectrum amplitude value vector a:
wherein ,
is the real amplitude value corresponding to the j-th pixel point, and normalized space-time spectrum amplitude value vector a= [ a ] 1 ,a 2 ,...,a NI ] T In a j As the weighting weight corresponding to the j-th pixel point.
Further, in the step S3, the clutter track is fitted by using a weighted least square method, and the corresponding objective function is as follows:
wherein ,
representing the solution to the function f (p 1 ,p 2 ) The value of ψ at minimum, +.>
/>
Lambda is the airborne radar wavelength, f r Is pulse repetition frequency, v is carrier flight speed, ψ is carrier yaw angle, +.>
Is the pitch angle of the clutter scatterer.
Further, the prior clutter plus noise covariance matrix in step S4 satisfies the following formula:
wherein ,
δ n 2 the noise power psi is obtained by sparse reconstruction space-time spectrum estimation opt The optimal solution of psi is obtained according to the weighted least square method, and I represents a unit array of NK multiplied by NK dimension.
Further, the step S5 is specifically implemented by the following steps:
s51, sampling data X by utilizing NK X r dimension column full rank dimension reduction transformation matrix T l And performing dimension reduction processing, wherein r is the degree of freedom of the self-adaptive processing after dimension reduction.
S52, calculating a target guide vector S after dimension reduction T Data vector X lT Noise covariance matrix R 0T ：
S T ＝T H S (8)
X lT ＝T H X l (9)
R 0T ＝T H R 0 T (10)
Wherein S is a target guide vector without dimension reduction, X l Is the received signal of the first distance unit, R 0 Is the a priori clutter plus noise covariance matrix.
S53, meterCalculating the optimal self-adaptive weight w after dimension reduction processing T ：
wherein ,μT Is a normalized complex constant that is used to normalize the complex constant,
s54, calculating a two-dimensional filtering output Z according to the optimal self-adaptive weight and the echo data vector out ：
The following advantages can be obtained by adopting the technical means:
according to the KA-STAP (high-precision-STAP) noise covariance matrix high-precision estimation method based on sparse reconstruction, short-range echoes of an airborne non-positive side view array radar have serious distance dependence, if a distance sampling statistical averaging method is adopted to estimate the clutter covariance matrix, the accuracy of clutter covariance matrix estimation is rapidly reduced, the sparse reconstruction is utilized to obtain a high-resolution two-dimensional space-time spectrum as priori knowledge, clutter tracks are fitted, and a clutter and noise covariance matrix is constructed and used for STAP weight calculation and self-adaptive filtering.
Drawings
FIG. 1 is a flow chart of a KA-STAP noise covariance matrix high-precision estimation method based on sparse reconstruction.
FIG. 2 is a graph of airborne radar configuration and clutter geometry in the method of the present invention.
FIG. 3 is a sparse reconstructed two-dimensional space-time average spectrum of the method of the present invention.
FIG. 4 is a graph of clutter trace fit for the method of the invention.
Fig. 5 is a graph of the filtered output after weights are calculated using a priori clutter plus noise covariance matrix.
Fig. 6 is a graph of the filtered output after weights are calculated using the training sample estimation covariance matrix.
Where H in fig. 2 is the aircraft flight height, ψ is the yaw angle, α is the clutter incidence cone angle of the scatterer P, β is the angle between the scatterer P and the antenna axis, θ is the azimuth angle of the scatterer P,
the pitch angle, v, of the diffuser P is the carrier flight speed.
Detailed Description
The technical scheme of the invention is further described below with reference to the accompanying drawings:
as shown in FIG. 1, the KA-STAP noise covariance matrix high-precision estimation method based on sparse reconstruction specifically comprises the following steps:
s1, analyzing echo data of an airborne radar, and acquiring a high-resolution two-dimensional space-time spectrum by utilizing sparse reconstruction;
s2, screening pixel points on the two-dimensional space-time spectrum, and calculating a weighting value corresponding to the pixel points;
s3, fitting the clutter track by using a weighted least square method;
s4, estimating noise power according to the sparse reconstruction space-time spectrum, and constructing a priori clutter plus noise covariance matrix;
s5, performing self-adaptive filtering and target detection on the detection unit by using the prior noise covariance matrix and the dimension reduction STAP.
Analyzing clutter characteristics of the airborne non-positive side view array radar, as shown in fig. 2, the airborne radar antenna is a uniform linear array, the number of antenna array elements is N, the number of time domain pulses in one coherent processing interval is K, the array element interval is d, the radar wavelength is lambda, the array element interval is d, and the radar wavelength is lambda, and d=lambda/2. The carrier flies at a constant speed v along the X axis, the yaw angle is psi, the flying height is H, alpha and beta are respectively the clutter incidence cone angle of the scatterer P and the included angle between the scatterer P and the antenna axial direction, theta,
The azimuth and pitch angles of the scatterer P, respectively.
The clutter in a certain range ring of the airborne radar can be equally divided into N according to azimuth angles c A plurality of impurity wave sources are arranged,
is the normalized spatial frequency of the ith impurity wave source,/>
Is normalized Doppler frequency, and respectively satisfies the following formulas:
the motion of the airborne radar platform can cause the space-time two-dimensional coupling characteristic of ground clutter, so that the clutter incidence cone angle cosine cos alpha of the scatterer P and the included angle cosine cos beta of the scatterer P and the antenna axial direction are in mutual connection, and the clutter space-time distribution track meets the formula:
the above is expressed as relative to the normalized Doppler frequency f d And normalized spatial frequency f s Is defined by the equation:
when 0 DEG < psi < 90 DEG, the clutter track is a cluster of inclined ellipses according to the formula, and of course, in an actual airborne radar antenna, the effect of the backward radiation of the antenna is small, so that the actual clutter track is half of an ellipse.
Receive signal X of the first distance unit l The pulse-by-pulse arrangement can be obtained:
X l ＝[S l_1 S l_2 …S l_K ] N×K (16)
wherein ,Sl_i Is the i-th distance unit and each array element of the i-th pulse receives a signal vector, l=1, 2, …, M, i=1, 2, …, K, M is the total number of distance units.
Selecting a window function T f ＝[w(1),w(2),…,w(i),…,w(K)]To perform signal windowing, w (i) is a window function coefficient to construct a windowing matrix T w The following are provided:
windowed receive signal T_X l The formula is satisfied:
T_X l ＝X l ·T w ＝[S Tl_1 …S Tl_i …S Tl_K ] N×K (18)
wherein ,STl_i Is the output signal of the ith pulse of the ith distance unit after windowing processing of each array element.
Transforming matrix F using FFT domain D Obtaining an array element-Doppler domain output signal:
D_X l ＝T_X l ·F D ＝[S Dl_1 …S Dl_i …S Dl_K ] N×K (19)
wherein ,SDl_i Indicating the output signal of each array element of the ith Doppler unit of the ith distance unit.
S is optimized by utilizing convex algorithm Dl _ i And performing sparse reconstruction, wherein a constraint equation of the sparse reconstruction is as follows:
wherein ,σi Is the spatial amplitude distribution of the signal received by the ith Doppler unit of the ith range unit,
is the value of the variable x corresponding to the function g (x) taking the minimum value, and observes the matrix ψ i Is an overcomplete base composed of airspace guide vectors, and the size is N multiplied by N s ，N s Is the number of units of space domain quantization, I.I 1 Represents L 1 Norm operation, epsilon i Is an allowable error.
The space domain sparse reconstruction is carried out on the array element-Doppler domain data by Doppler units by utilizing a formula (20), a two-dimensional space-time spectrum with high resolution can be obtained and is marked as A (f) d ,f s), wherein fd 、f s The normalized doppler frequency and normalized spatial frequency of the clutter scattering unit, respectively.
Selecting the pixel point with the largest amplitude on each normalized spatial frequency on a two-dimensional space-time spectrum, recording the coordinates of the pixel point, removing noise pixel points deviating from clutter tracks in the selected pixel points, and obtaining a screened point set { (x) j ,y j )}，x j Is the abscissa of the jth pixel point, y j Is the ordinate of the j-th pixel, j=1, 2, 3..n I ，N I The number of the pixel points meeting the requirements is selected.
Marking clutter amplitude on the space-time spectrum corresponding to each pixel point in the screened pixel point set as A j Using recovery coefficients
Correcting the windowing effect to obtain corrected amplitude A' j The method comprises the following steps:
A j '＝A j /w (21)
because FFT conversion can cause coherent accumulation of signal amplitude in frequency domain, real amplitude value corresponding to pixel point
The method comprises the following steps: />
Then sometimes the spectrum isValue vector
Carrying out normalization processing on the space-time spectrum amplitude value vector A to obtain a normalized space-time spectrum amplitude value vector a:
using normalized space-time spectrum amplitude vectors
A of (a) j As the weighting weight corresponding to the j-th pixel point.
And fitting the clutter track by using a weighted least square method, wherein an objective function corresponding to the clutter track least square fitting is as follows:
wherein ,
representing the solution to the function f (p 1 ,p 2 ) The value of ψ at minimum, +.>
f r Is pulse repetition frequency, v is carrier flight speed, ψ is carrier yaw angle, +.>
Is the pitch angle of clutter scatterer, < >>
Can be obtained by accurate measurement of a radar altimeter.
In order to make the function f (p 1 ,p 2 ) Take the minimum value, must have
Namely:
wherein ,
respectively representing the function f (p 1 ,p 2 ) Relative to p 1 、p 2 Is a partial derivative of (c).
The simultaneous formulas (25) and (26) can obtain p 1 Is the optimal solution of (a):
thereby obtaining the optimal solution psi of psi opt The method comprises the following steps:
estimating noise power delta according to sparse reconstruction space-time spectrum n 2 The prior clutter plus noise covariance matrix satisfies the formula:
wherein ,
i represents a unit array of NK×NK dimensions.
And performing dimension reduction processing on the sampled data by using an NK x r dimension column full rank dimension reduction transformation matrix T, wherein r is the degree of freedom of the self-adaptive processing after dimension reduction. Target guide vector S after dimension reduction T Data vector X lT Noise covariance matrix R 0T The method comprises the following steps of:
S T ＝T H S (31)
X lT ＝T H X l (32)
R 0T ＝T H R 0 T (33)
wherein S is a target guide vector without dimension reduction, X l Is the received signal of the first distance unit, R 0 Is the a priori clutter plus noise covariance matrix.
Further, the optimal self-adaptive weight w after the dimension reduction processing is calculated T ：
wherein ,μT Is a normalized complex constant that is used to normalize the complex constant,
the two-dimensional filtering output Z can be obtained according to the optimal self-adaptive weight and the echo data vector out ：
The effectiveness of the method is further verified through simulation experiments, and parameters of the simulation experiments of the airborne radar system are shown in table 1.
X l The signal is received for the 360 th range cell with a skew of 10.8km. The detection unit is injected with the target to be detected, the signal-to-noise ratio is-10 dB, and the normalized Doppler frequency and the normalized spatial frequency are respectively 0.0313 and 0.2734.Sparse reconstruction is carried out on the unit to be detected and two adjacent protection units, the sparse reconstruction two-dimensional space-time average spectrum is used as priori knowledge, clutter track fitting is carried out according to a clutter covariance matrix estimation algorithm, and fig. 3 and fig. 4 are sparse reconstruction two-dimensional space-time average spectrum and clutter track fitting graphs respectively.
The filtered output of the target detection after calculating the STAP weight by using the prior clutter plus noise covariance matrix is shown in fig. 5, the filtered output of the target detection after calculating the STAP weight by using the training sample estimated covariance matrix is shown in fig. 6, the training sample is 120 distance units adjacent to the unit to be detected, and the target detection method adopts a Doppler three-channel joint adaptive processing (3 DT-STAP) algorithm. In contrast to fig. 5 and fig. 6, due to the non-stationarity of the distance between short-range clutter, the data of each distance unit no longer satisfies the IID condition, which results in a rapid increase of the covariance matrix error directly estimated by the training sample, a serious decrease of the clutter suppression performance, and the target cannot be effectively detected.
Table 1 radar system parameters
| Parameter name | Parameter values |
| Number of array elements | 16 |
| Total number of distance units | 1000 |
| Number of coherent accumulated pulses | 128 |
| Pulse repetition frequency | 5000HZ |
| Sampling bandwidth | 5MHZ |
| Speed of carrier | 130m/s |
| Flying height of carrier | 8000m |
| Array element spacing and wavelength ratio | 1/2 |
The embodiments of the present invention have been described in detail with reference to the drawings, but the present invention is not limited to the above embodiments, and various changes can be made within the knowledge of those skilled in the art without departing from the spirit of the present invention.
Claims (4)
1. A KA-STAP (high-precision-STAP) noise covariance matrix high-precision estimation method based on sparse reconstruction is characterized by comprising the following steps:
s1, analyzing echo data of an airborne radar, and acquiring a high-resolution two-dimensional space-time spectrum by utilizing sparse reconstruction;
s2, screening pixel points on the two-dimensional space-time spectrum, and calculating a weighting value corresponding to the pixel points;
s3, fitting the clutter track by using a weighted least square method;
s4, estimating noise power according to the sparse reconstruction space-time spectrum, and constructing a priori clutter plus noise covariance matrix;
s5, performing self-adaptive filtering and target detection on the detection unit by using the priori noise covariance matrix and the dimension reduction STAP;
the step S2 is specifically implemented by the following steps:
s21, selecting the pixel point with the largest amplitude value on each normalized space frequency on a two-dimensional space-time spectrum and recording the coordinates of the pixel point;
s22, removing noise pixel points deviating from clutter tracks in the pixel points selected in the S21 to obtain a filtered point set { (x) j ,y j )}，x j Is the abscissa of the jth pixel point, y j Is the ordinate of the j-th pixel, j=1, 2, 3..n I ，N I The number of the pixel points which meet the requirements is selected;
s23, carrying out normalization processing on the space-time spectrum amplitude value vector A to obtain a normalized space-time spectrum amplitude value vector a:
wherein ,
is the real amplitude value corresponding to the j-th pixel point, and normalized space-time spectrum amplitude value vector is used for +.>
In a j As the weighting weight corresponding to the j-th pixel point;
the step S5 is specifically realized by the following steps:
s51, sampling data X by utilizing NK X r dimension column full rank dimension reduction transformation matrix T l Performing dimension reduction treatment, wherein r is the degree of freedom of self-adaptive treatment after dimension reduction;
s52, calculating a target guide vector S after dimension reduction T Data vector X lT Noise covariance matrix R 0T ：
S T ＝T H S
X lT ＝T H X l
R 0T ＝T H R 0 T
Wherein S is a target guide vector without dimension reduction, X l Is the received signal of the first distance unit, l=1, 2, …, M is the total number of distance units, R 0 Is a priori clutter plus noise covariance matrix;
s53, calculating an optimal self-adaptive weight w after the dimension reduction processing T ：
wherein ,μT Is a normalized complex constant that is used to normalize the complex constant,
s54, calculating a two-dimensional filtering output Z according to the optimal self-adaptive weight and the echo data vector out ：
2. The high-precision estimation method of the KA-STAP noise covariance matrix based on sparse reconstruction according to claim 1, wherein the step S1 is specifically realized by the following steps:
s11, windowing echo signals received by the airborne radar, and receiving signals T_X after processing l The formula is satisfied:
T_X l ＝X l ·T w ＝[S Tl_1 …S Tl_i ···S Tl_K ] N×K
wherein ,Xl Is the received signal of the first distance unit, T w Is based on window function T f ＝[w(1),w(2),···,w(i),…,w(K)]To pair(s)K x K-dimensional windowing matrix of corner elements, w (i) is a window function coefficient, S Tl_i The method is characterized in that the method comprises the steps that output signals after windowing processing of each array element of an ith pulse of a first distance unit are l=1, 2, …, M, i=1, 2, …, K, M is the total number of the distance units, N is the number of array elements of a radar antenna, and K is the number of time domain pulses in a primary coherent processing interval;
s12, transforming matrix F by FFT domain D Obtaining an array element-Doppler domain output signal:
D_X l ＝T_X l ·F D ＝[S Dl_1 …S Dl_i …S Dl_K ] N×K
wherein ,SDl_i Indicating the output signals of each array element of the ith Doppler unit of the ith distance unit;
s13, utilizing a convex optimization algorithm to perform S Dl_i And performing sparse reconstruction, wherein a constraint equation of the sparse reconstruction is as follows:
wherein ,σi Is the spatial amplitude distribution of the signal received by the ith Doppler unit of the ith range unit,
is the value of the variable x corresponding to the function g (x) taking the minimum value, and observes the matrix ψ i Is an overcomplete base composed of airspace guide vectors, and the size is N multiplied by N s ，N s Is the number of units of space domain quantization, I.I 1 Represents L 1 Norm operation, epsilon i Is an allowable error;
s14, performing space domain sparse reconstruction on the array element-Doppler domain data unit by unit Doppler to obtain a high-resolution two-dimensional space-time spectrum.
3. The high-precision estimation method of KA-STAP (hybrid-start-stop) noise covariance matrix based on sparse reconstruction according to claim 1, wherein step S3 utilizes a weighted least square method to fit clutter tracks, and the corresponding objective function is as follows:
wherein ,
representing the solution to the function f (p 1 ,p 2 ) The value of psi at which the minimum value is taken,
lambda is the airborne radar wavelength, f r Is pulse repetition frequency, v is carrier flight speed, ψ is carrier yaw angle, +.>
Is the pitch angle of the clutter scatterer.
4. The high-precision estimation method of the KA-STAP noise covariance matrix based on sparse reconstruction of claim 1, wherein the prior clutter plus noise covariance matrix in step S4 satisfies the following formula:
wherein ,
δ n 2 the noise power psi is obtained by sparse reconstruction space-time spectrum estimation opt The optimal solution of psi is obtained according to the weighted least square method, and I represents a unit array of NK multiplied by NK dimension.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201810896127.4A CN109212500B (en) | 2018-08-08 | 2018-08-08 | High-precision KA-STAP (K-ary-based adaptive-noise) covariance matrix estimation method based on sparse reconstruction |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201810896127.4A CN109212500B (en) | 2018-08-08 | 2018-08-08 | High-precision KA-STAP (K-ary-based adaptive-noise) covariance matrix estimation method based on sparse reconstruction |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN109212500A CN109212500A (en) | 2019-01-15 |
| CN109212500B true CN109212500B (en) | 2023-05-16 |
Family
ID=64988834
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN201810896127.4A Active CN109212500B (en) | 2018-08-08 | 2018-08-08 | High-precision KA-STAP (K-ary-based adaptive-noise) covariance matrix estimation method based on sparse reconstruction |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN109212500B (en) |
Families Citing this family (10)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN110297247B (en) * | 2019-07-22 | 2022-06-24 | 河海大学 | Meteorological radar wind power plant clutter suppression method based on low-rank matrix sparse recovery |
| CN110658517B (en) * | 2019-10-11 | 2021-08-31 | 深圳大学 | Dimensionality reduction sparse STAP method and device based on uncertain priori knowledge |
| WO2021068197A1 (en) * | 2019-10-11 | 2021-04-15 | 深圳大学 | Dimensionality-reduction sparse stap method and apparatus based on uncertain priori knowledge |
| CN110764069B (en) * | 2019-11-14 | 2021-08-10 | 内蒙古工业大学 | Sparse recovery STAP color loading method based on knowledge assistance |
| CN111474526B (en) * | 2020-04-24 | 2022-03-08 | 成都航空职业技术学院 | Rapid reconstruction method of airborne STAP clutter covariance matrix |
| CN111537975B (en) * | 2020-05-26 | 2022-03-08 | 成都航空职业技术学院 | High-freedom airborne radar space-time adaptive signal processing method |
| CN111537976B (en) * | 2020-07-01 | 2022-12-09 | 内蒙古工业大学 | Moving target detection method and device of airborne radar |
| CN111580065B (en) * | 2020-07-06 | 2022-11-08 | 内蒙古工业大学 | Sparse STAP recovery method based on knowledge assistance |
| CN112800497B (en) * | 2020-12-28 | 2023-08-11 | 西安电子科技大学 | Airborne three-dimensional heterogeneous array clutter suppression method based on sparse spectrum recovery |
| CN116559819B (en) * | 2023-07-07 | 2023-09-15 | 中国人民解放军空军预警学院 | Airborne radar knowledge auxiliary color loading clutter suppression method and device |
Family Cites Families (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN103454624B (en) * | 2013-09-22 | 2016-01-20 | 河海大学 | The direct data domain moving target detection method of spectrum time empty based on dimensionality reduction sparse reconstruct |
| CN106646418B (en) * | 2017-01-16 | 2019-04-16 | 河海大学 | A kind of airborne radar clutter space-time spectrum method for quick estimating based on improvement OMP |
-
2018
- 2018-08-08 CN CN201810896127.4A patent/CN109212500B/en active Active
Also Published As
| Publication number | Publication date |
|---|---|
| CN109212500A (en) | 2019-01-15 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN109212500B (en) | High-precision KA-STAP (K-ary-based adaptive-noise) covariance matrix estimation method based on sparse reconstruction | |
| CN109116311B (en) | Clutter suppression method based on knowledge-aided sparse iteration covariance estimation | |
| CN103149561B (en) | Microwave imaging method based on scenario block sparsity | |
| CN112612006B (en) | Deep learning-based non-uniform clutter suppression method for airborne radar | |
| CN110174650B (en) | Meteorological radar wind power plant clutter suppression method based on two-dimensional joint interpolation | |
| CN113253213B (en) | Method and system for measuring ionosphere echo spatial distribution characteristics of high-frequency ground wave radar | |
| CN109324315B (en) | Space-time adaptive radar clutter suppression method based on double-layer block sparsity | |
| CN101881822B (en) | Method for inhibiting same frequency interference of shared-spectrum radars | |
| CN112612005B (en) | Radar main lobe interference resistance method based on deep learning | |
| CN108761419A (en) | Low level wind shear velocity estimation method based on combination main channel self-adaptive processing when empty | |
| CN109507666B (en) | ISAR sparse band imaging method based on off-network variational Bayesian algorithm | |
| CN108931766B (en) | Non-uniform STAP interference target filtering method based on sparse reconstruction | |
| CN111273238A (en) | SAR (synthetic aperture radar) wide-band and narrow-band interference simultaneous inhibition method based on low-rank recovery | |
| CN106569191A (en) | Method of acquiring target RCS by using high resolution imaging | |
| CN108387884B (en) | Airborne radar clutter suppression method based on knowledge-assisted sparse progressive minimum variance | |
| CN109799495A (en) | A kind of broadband delay time estimation method for high-fidelity ARRAY PROCESSING | |
| CN107576947B (en) | L-shaped array pair coherent information source two-dimensional direction of arrival estimation method based on time smoothing | |
| CN108020817A (en) | Air-borne Forward-looking battle array radar clutter suppression method based on registration | |
| CN111913157A (en) | Sea clutter suppression method based on radar signal space-time decorrelation model | |
| CN109188373B (en) | Main lobe interference resisting method based on subarray blocking matrix preprocessing | |
| CN113466797B (en) | Double-base SAR space-time clutter suppression method based on clutter ridge matching sparse recovery | |
| CN106802408B (en) | Airborne non-positive side array short range clutter distance fuzzy suppression method based on sparse recovery | |
| CN110764066B (en) | Target detection method based on real signal subspace under existence of error | |
| CN113189570B (en) | Array signal processing method and system based on complex domain compressed sensing | |
| CN116008925A (en) | Improved target radar sectional area estimation algorithm |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| GR01 | Patent grant | ||
| GR01 | Patent grant |